Evaluation of syndromic surveillance systems--design of an epidemic simulation model.

INTRODUCTION

The paucity of outbreak data from biologic terrorism and emerging infectious diseases limits the evaluation of syndromic surveillance systems. Evaluation using naturally occurring outbreaks of proxy disease (e.g., influenza) is one alternative but does not allow for rigorous evaluation. Another approach is to inject simulated outbreaks into real background data, but existing simulation models generally do not account for such factors as spatial mobility and do not explicitly incorporate knowledge of the disease agent.

OBJECTIVE

The objective of this analysis was to design a simulated anthrax epidemic injection model that accounts for the complexity of the background data and enables sensitivity analyses based on uncertain disease-agent characteristics. MODEL REQUIREMENTS AND ASSUMPTIONS: Model requirements are described and used to limit the scope of model development. Major assumptions used to limit model complexity are also described. Available literature on inhalational anthrax is reviewed to ensure that the level of model detail reflects available disease knowledge. MODEL DESIGN: The model is divided into four components: 1) agent dispersion, 2) infection, 3) disease and behavior, and 4) data source. The agent-dispersion component uses a Gaussian plume model to compute spore counts on a fine grid. The infection component uses a cohort approach to identify infected persons by residential zip code, accounting for demographic covariates and spatial mobility. The disease and behavior component uses a discrete-event approach to simulate progression through disease stages and health-services utilization. The data-source component generates records to insert into background data sources.

CONCLUSIONS

An epidemic simulation model was designed to enable evaluation of syndromic surveillance systems. The model addresses limitations of existing simulation approaches by accounting for such factors as spatial mobility and by explicitly modeling disease knowledge. Subsequent work entails software implementation and model validation.